




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase   :			Any          = get_tests_dir("fixtures/test_sentencepiece.model")
__UpperCamelCase   :			Tuple          = get_tests_dir("fixtures/test_sentencepiece_bpe.model")

__UpperCamelCase   :			Tuple          = """pt""" if is_torch_available() else """tf"""

@require_sentencepiece
@require_tokenizers
class 						__lowerCAmelCase      (      a_ ,			unittest.TestCase					):
							UpperCamelCase__   	=    CamembertTokenizer
							UpperCamelCase__   	=    CamembertTokenizerFast
							UpperCamelCase__   	=    True
							UpperCamelCase__   	=    True
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								super().setUp()

								# We have a SentencePiece fixture for testing
								a									=				CamembertTokenizer(UpperCamelCase_							)
								tokenizer.save_pretrained(self.tmpdirname							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				"""<pad>"""
								a									=				1

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_							)						, UpperCamelCase_							)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_							)						, UpperCamelCase_							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				list(self.get_tokenizer().get_vocab().keys()							)

								self.assertEqual(vocab_keys[0]						, """<s>NOTUSED"""							)
								self.assertEqual(vocab_keys[1]						, """<pad>"""							)
								self.assertEqual(vocab_keys[-1]						, """<mask>"""							)
								self.assertEqual(len(UpperCamelCase_							)						, 1004							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								self.assertEqual(self.get_tokenizer().vocab_size						, 1005							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				CamembertTokenizer(UpperCamelCase_							)
								tokenizer.save_pretrained(self.tmpdirname							)
								a									=				CamembertTokenizerFast.from_pretrained(self.tmpdirname							)

								a									=				"""I was born in 92000, and this is falsé."""

								a									=				tokenizer.encode(UpperCamelCase_							)
								a									=				rust_tokenizer.encode(UpperCamelCase_							)
								self.assertListEqual(UpperCamelCase_						, UpperCamelCase_							)

								a									=				tokenizer.encode(UpperCamelCase_						, add_special_tokens=UpperCamelCase_							)
								a									=				rust_tokenizer.encode(UpperCamelCase_						, add_special_tokens=UpperCamelCase_							)
								self.assertListEqual(UpperCamelCase_						, UpperCamelCase_							)

								# <unk> tokens are not the same for `rust` than for `slow`.
								# Because spm gives back raw token instead of `unk` in EncodeAsPieces
								# tokens = tokenizer.tokenize(sequence)
								a									=				tokenizer.convert_ids_to_tokens(UpperCamelCase_							)
								a									=				rust_tokenizer.tokenize(UpperCamelCase_							)
								self.assertListEqual(UpperCamelCase_						, UpperCamelCase_							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								if not self.test_rust_tokenizer:
									return

								a									=				self.get_tokenizer()
								a									=				self.get_rust_tokenizer()

								a									=				"""I was born in 92000, and this is falsé."""

								a									=				tokenizer.tokenize(UpperCamelCase_							)
								a									=				rust_tokenizer.tokenize(UpperCamelCase_							)
								self.assertListEqual(UpperCamelCase_						, UpperCamelCase_							)

								a									=				tokenizer.encode(UpperCamelCase_						, add_special_tokens=UpperCamelCase_							)
								a									=				rust_tokenizer.encode(UpperCamelCase_						, add_special_tokens=UpperCamelCase_							)
								self.assertListEqual(UpperCamelCase_						, UpperCamelCase_							)

								a									=				self.get_rust_tokenizer()
								a									=				tokenizer.encode(UpperCamelCase_							)
								a									=				rust_tokenizer.encode(UpperCamelCase_							)
								self.assertListEqual(UpperCamelCase_						, UpperCamelCase_							)





							@slow
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				{"""input_ids""": [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
								# fmt: on

								# camembert is a french model. So we also use french texts.
								a									=				[
								    """Le transformeur est un modèle d\'apprentissage profond introduit en 2017, """
								    """utilisé principalement dans le domaine du traitement automatique des langues (TAL).""",
								    """À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus """
								    """pour gérer des données séquentielles, telles que le langage naturel, pour des tâches """
								    """telles que la traduction et la synthèse de texte.""",
								]

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCamelCase_						, model_name="""camembert-base"""						, revision="""3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf"""						, sequences=UpperCamelCase_						, )









"""simple docstring"""
from maths.prime_check import is_prime


def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if not isinstance(__snake_case ,     __snake_case  ):
              __magic_name__      				=    F"""Input value of [number={number}] must be an integer"""
              raise TypeError(__snake_case  )
       if is_prime(__snake_case  ) and is_prime(number + 2  ):
              return number + 2
       else:
              return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()